{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 单车共享数据探索\n",
    "本项目将原始数据集存为csv格式，方便调用pandas做数据分析。\n",
    "字段说明 Instant记录号 Dteday：日期 Season：季节（1=春天、2=夏天、3=秋天、4=冬天） yr：年份，(0: 2011, 1:2012) mnth：月份( 1 to 12) hr：小时 (0 to 23) （只在hour.csv有，作业忽略此字段） holiday：是否是节假日 weekday：星期中的哪天，取值为0～6 workingday：是否工作日 1=工作日 （是否为工作日，1为工作日，0为非周末或节假日 weathersit：天气（1：晴天，多云 ",
    "2：雾天，阴天 ",
    "3：小雪，小雨 ",
    "4：大雨，大雪，大雾） temp：气温摄氏度 atemp：体感温度 hum：湿度 windspeed：风速\n",
    "casual：非注册用户个数 registered：注册用户个数 cnt：给定日期（天）时间（每小时）总租车人数，响应变量y casual、registered和cnt三个特征均为要预测的y，作业里只需对cnt进行预测"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 导入必要的工具包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from __future__ import print_function\n",
    "from __future__ import division\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.344167</td>\n",
       "      <td>0.363625</td>\n",
       "      <td>0.805833</td>\n",
       "      <td>0.160446</td>\n",
       "      <td>331</td>\n",
       "      <td>654</td>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.363478</td>\n",
       "      <td>0.353739</td>\n",
       "      <td>0.696087</td>\n",
       "      <td>0.248539</td>\n",
       "      <td>131</td>\n",
       "      <td>670</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-03</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.196364</td>\n",
       "      <td>0.189405</td>\n",
       "      <td>0.437273</td>\n",
       "      <td>0.248309</td>\n",
       "      <td>120</td>\n",
       "      <td>1229</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2011-01-04</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.212122</td>\n",
       "      <td>0.590435</td>\n",
       "      <td>0.160296</td>\n",
       "      <td>108</td>\n",
       "      <td>1454</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-05</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.226957</td>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.436957</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>82</td>\n",
       "      <td>1518</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant      dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
       "0        1  2011-01-01       1   0     1        0        6           0   \n",
       "1        2  2011-01-02       1   0     1        0        0           0   \n",
       "2        3  2011-01-03       1   0     1        0        1           1   \n",
       "3        4  2011-01-04       1   0     1        0        2           1   \n",
       "4        5  2011-01-05       1   0     1        0        3           1   \n",
       "\n",
       "   weathersit      temp     atemp       hum  windspeed  casual  registered  \\\n",
       "0           2  0.344167  0.363625  0.805833   0.160446     331         654   \n",
       "1           2  0.363478  0.353739  0.696087   0.248539     131         670   \n",
       "2           1  0.196364  0.189405  0.437273   0.248309     120        1229   \n",
       "3           1  0.200000  0.212122  0.590435   0.160296     108        1454   \n",
       "4           1  0.226957  0.229270  0.436957   0.186900      82        1518   \n",
       "\n",
       "    cnt  \n",
       "0   985  \n",
       "1   801  \n",
       "2  1349  \n",
       "3  1562  \n",
       "4  1600  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dpath = \"./Bike-Sharing-Dataset/\"\n",
    "data = pd.read_csv(dpath + \"day.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据基本信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 731 entries, 0 to 730\n",
      "Data columns (total 16 columns):\n",
      "instant       731 non-null int64\n",
      "dteday        731 non-null object\n",
      "season        731 non-null int64\n",
      "yr            731 non-null int64\n",
      "mnth          731 non-null int64\n",
      "holiday       731 non-null int64\n",
      "weekday       731 non-null int64\n",
      "workingday    731 non-null int64\n",
      "weathersit    731 non-null int64\n",
      "temp          731 non-null float64\n",
      "atemp         731 non-null float64\n",
      "hum           731 non-null float64\n",
      "windspeed     731 non-null float64\n",
      "casual        731 non-null int64\n",
      "registered    731 non-null int64\n",
      "cnt           731 non-null int64\n",
      "dtypes: float64(4), int64(11), object(1)\n",
      "memory usage: 91.5+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "instant       0\n",
       "dteday        0\n",
       "season        0\n",
       "yr            0\n",
       "mnth          0\n",
       "holiday       0\n",
       "weekday       0\n",
       "workingday    0\n",
       "weathersit    0\n",
       "temp          0\n",
       "atemp         0\n",
       "hum           0\n",
       "windspeed     0\n",
       "casual        0\n",
       "registered    0\n",
       "cnt           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 探索数据\n",
    "查看数据各特征的分布，以及特征之间是否存在相关关系等冗余。\n",
    "我们可以借用可视化工具来直观感觉数据的分布。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>366.000000</td>\n",
       "      <td>2.496580</td>\n",
       "      <td>0.500684</td>\n",
       "      <td>6.519836</td>\n",
       "      <td>0.028728</td>\n",
       "      <td>2.997264</td>\n",
       "      <td>0.683995</td>\n",
       "      <td>1.395349</td>\n",
       "      <td>0.495385</td>\n",
       "      <td>0.474354</td>\n",
       "      <td>0.627894</td>\n",
       "      <td>0.190486</td>\n",
       "      <td>848.176471</td>\n",
       "      <td>3656.172367</td>\n",
       "      <td>4504.348837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>211.165812</td>\n",
       "      <td>1.110807</td>\n",
       "      <td>0.500342</td>\n",
       "      <td>3.451913</td>\n",
       "      <td>0.167155</td>\n",
       "      <td>2.004787</td>\n",
       "      <td>0.465233</td>\n",
       "      <td>0.544894</td>\n",
       "      <td>0.183051</td>\n",
       "      <td>0.162961</td>\n",
       "      <td>0.142429</td>\n",
       "      <td>0.077498</td>\n",
       "      <td>686.622488</td>\n",
       "      <td>1560.256377</td>\n",
       "      <td>1937.211452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.059130</td>\n",
       "      <td>0.079070</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.022392</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>183.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.337083</td>\n",
       "      <td>0.337842</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>0.134950</td>\n",
       "      <td>315.500000</td>\n",
       "      <td>2497.000000</td>\n",
       "      <td>3152.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>366.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.498333</td>\n",
       "      <td>0.486733</td>\n",
       "      <td>0.626667</td>\n",
       "      <td>0.180975</td>\n",
       "      <td>713.000000</td>\n",
       "      <td>3662.000000</td>\n",
       "      <td>4548.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>548.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.655417</td>\n",
       "      <td>0.608602</td>\n",
       "      <td>0.730209</td>\n",
       "      <td>0.233214</td>\n",
       "      <td>1096.000000</td>\n",
       "      <td>4776.500000</td>\n",
       "      <td>5956.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>731.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.861667</td>\n",
       "      <td>0.840896</td>\n",
       "      <td>0.972500</td>\n",
       "      <td>0.507463</td>\n",
       "      <td>3410.000000</td>\n",
       "      <td>6946.000000</td>\n",
       "      <td>8714.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          instant      season          yr        mnth     holiday     weekday  \\\n",
       "count  731.000000  731.000000  731.000000  731.000000  731.000000  731.000000   \n",
       "mean   366.000000    2.496580    0.500684    6.519836    0.028728    2.997264   \n",
       "std    211.165812    1.110807    0.500342    3.451913    0.167155    2.004787   \n",
       "min      1.000000    1.000000    0.000000    1.000000    0.000000    0.000000   \n",
       "25%    183.500000    2.000000    0.000000    4.000000    0.000000    1.000000   \n",
       "50%    366.000000    3.000000    1.000000    7.000000    0.000000    3.000000   \n",
       "75%    548.500000    3.000000    1.000000   10.000000    0.000000    5.000000   \n",
       "max    731.000000    4.000000    1.000000   12.000000    1.000000    6.000000   \n",
       "\n",
       "       workingday  weathersit        temp       atemp         hum   windspeed  \\\n",
       "count  731.000000  731.000000  731.000000  731.000000  731.000000  731.000000   \n",
       "mean     0.683995    1.395349    0.495385    0.474354    0.627894    0.190486   \n",
       "std      0.465233    0.544894    0.183051    0.162961    0.142429    0.077498   \n",
       "min      0.000000    1.000000    0.059130    0.079070    0.000000    0.022392   \n",
       "25%      0.000000    1.000000    0.337083    0.337842    0.520000    0.134950   \n",
       "50%      1.000000    1.000000    0.498333    0.486733    0.626667    0.180975   \n",
       "75%      1.000000    2.000000    0.655417    0.608602    0.730209    0.233214   \n",
       "max      1.000000    3.000000    0.861667    0.840896    0.972500    0.507463   \n",
       "\n",
       "            casual   registered          cnt  \n",
       "count   731.000000   731.000000   731.000000  \n",
       "mean    848.176471  3656.172367  4504.348837  \n",
       "std     686.622488  1560.256377  1937.211452  \n",
       "min       2.000000    20.000000    22.000000  \n",
       "25%     315.500000  2497.000000  3152.000000  \n",
       "50%     713.000000  3662.000000  4548.000000  \n",
       "75%    1096.000000  4776.500000  5956.000000  \n",
       "max    3410.000000  6946.000000  8714.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### cnt 变量分布分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEFCAYAAAD5bXAgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x125cac18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(data.cnt.values,bins = 60,kde=True)\n",
    "plt.xlabel('cnt', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "season属性的不同取值和出现的次数\n",
      "3    188\n",
      "2    184\n",
      "1    181\n",
      "4    178\n",
      "Name: season, dtype: int64\n",
      "\n",
      "mnth属性的不同取值和出现的次数\n",
      "12    62\n",
      "10    62\n",
      "8     62\n",
      "7     62\n",
      "5     62\n",
      "3     62\n",
      "1     62\n",
      "11    60\n",
      "9     60\n",
      "6     60\n",
      "4     60\n",
      "2     57\n",
      "Name: mnth, dtype: int64\n",
      "\n",
      "weathersit属性的不同取值和出现的次数\n",
      "1    463\n",
      "2    247\n",
      "3     21\n",
      "Name: weathersit, dtype: int64\n",
      "\n",
      "weekday属性的不同取值和出现的次数\n",
      "6    105\n",
      "1    105\n",
      "0    105\n",
      "5    104\n",
      "4    104\n",
      "3    104\n",
      "2    104\n",
      "Name: weekday, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#对类别型特征，观察其取值范围及直方图\n",
    "categorical_features = ['season','mnth','weathersit','weekday']\n",
    "for col in categorical_features:\n",
    "    print('\\n%s属性的不同取值和出现的次数'%col)\n",
    "    print(data[col].value_counts())\n",
    "    data[col] = data[col].astype('object')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 两两特征之间的相关性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11, 11)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_corr = data.corr().abs()\n",
    "data_corr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAyMAAANRCAYAAADwOWMPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14670dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(15, 15))\n",
    "sns.heatmap(data_corr,annot=True)\n",
    "\n",
    "# Mask unimportant features\n",
    "sns.heatmap(data_corr, mask=data_corr < 1, cbar=False)\n",
    "\n",
    "plt.savefig('bike_coor.png' )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mnth and holiday = 0.99\n",
      "temp and atemp = 0.95\n",
      "instant and dteday = 0.87\n",
      "weathersit and atemp = 0.67\n",
      "instant and temp = 0.66\n",
      "holiday and atemp = 0.63\n",
      "instant and atemp = 0.63\n",
      "mnth and atemp = 0.63\n",
      "dteday and temp = 0.59\n",
      "dteday and atemp = 0.57\n",
      "holiday and temp = 0.54\n",
      "holiday and weathersit = 0.54\n",
      "mnth and weathersit = 0.54\n",
      "mnth and temp = 0.54\n",
      "yr and weathersit = 0.52\n"
     ]
    }
   ],
   "source": [
    "cols = data.columns\n",
    "#Set the threshold to select only highly correlated attributes\n",
    "threshold = 0.5\n",
    "# List of pairs along with correlation above threshold\n",
    "corr_list = []\n",
    "#size = data.shape[1]\n",
    "size = data_corr.shape[0]\n",
    "\n",
    "#Search for the highly correlated pairs\n",
    "for i in range(0, size): #for 'size' features\n",
    "    for j in range(i+1,size): #avoid repetition\n",
    "        if (data_corr.iloc[i,j] >= threshold and data_corr.iloc[i,j] < 1) or (data_corr.iloc[i,j] < 0 and data_corr.iloc[i,j] <= -threshold):\n",
    "            corr_list.append([data_corr.iloc[i,j],i,j]) #store correlation and columns index\n",
    "\n",
    "#Sort to show higher ones first            \n",
    "s_corr_list = sorted(corr_list,key=lambda x: -abs(x[0]))\n",
    "\n",
    "#Print correlations and column names\n",
    "for v,i,j in s_corr_list:\n",
    "    print (\"%s and %s = %.2f\" % (cols[i],cols[j],v))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### 特征工程"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season_1</th>\n",
       "      <th>season_2</th>\n",
       "      <th>season_3</th>\n",
       "      <th>season_4</th>\n",
       "      <th>mnth_1</th>\n",
       "      <th>mnth_2</th>\n",
       "      <th>mnth_3</th>\n",
       "      <th>mnth_4</th>\n",
       "      <th>mnth_5</th>\n",
       "      <th>mnth_6</th>\n",
       "      <th>...</th>\n",
       "      <th>weathersit_1</th>\n",
       "      <th>weathersit_2</th>\n",
       "      <th>weathersit_3</th>\n",
       "      <th>weekday_0</th>\n",
       "      <th>weekday_1</th>\n",
       "      <th>weekday_2</th>\n",
       "      <th>weekday_3</th>\n",
       "      <th>weekday_4</th>\n",
       "      <th>weekday_5</th>\n",
       "      <th>weekday_6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   season_1  season_2  season_3  season_4  mnth_1  mnth_2  mnth_3  mnth_4  \\\n",
       "0         1         0         0         0       1       0       0       0   \n",
       "1         1         0         0         0       1       0       0       0   \n",
       "2         1         0         0         0       1       0       0       0   \n",
       "3         1         0         0         0       1       0       0       0   \n",
       "4         1         0         0         0       1       0       0       0   \n",
       "\n",
       "   mnth_5  mnth_6    ...      weathersit_1  weathersit_2  weathersit_3  \\\n",
       "0       0       0    ...                 0             1             0   \n",
       "1       0       0    ...                 0             1             0   \n",
       "2       0       0    ...                 1             0             0   \n",
       "3       0       0    ...                 1             0             0   \n",
       "4       0       0    ...                 1             0             0   \n",
       "\n",
       "   weekday_0  weekday_1  weekday_2  weekday_3  weekday_4  weekday_5  weekday_6  \n",
       "0          0          0          0          0          0          0          1  \n",
       "1          1          0          0          0          0          0          0  \n",
       "2          0          1          0          0          0          0          0  \n",
       "3          0          0          1          0          0          0          0  \n",
       "4          0          0          0          1          0          0          0  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#类别特征  独热编码\n",
    "categorical_data_cat = data[categorical_features]\n",
    "categorical_data_cat = pd.get_dummies(categorical_data_cat)\n",
    "categorical_data_cat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season_1</th>\n",
       "      <th>season_2</th>\n",
       "      <th>season_3</th>\n",
       "      <th>season_4</th>\n",
       "      <th>mnth_1</th>\n",
       "      <th>mnth_2</th>\n",
       "      <th>mnth_3</th>\n",
       "      <th>mnth_4</th>\n",
       "      <th>mnth_5</th>\n",
       "      <th>mnth_6</th>\n",
       "      <th>...</th>\n",
       "      <th>weathersit_3</th>\n",
       "      <th>weekday_0</th>\n",
       "      <th>weekday_1</th>\n",
       "      <th>weekday_2</th>\n",
       "      <th>weekday_3</th>\n",
       "      <th>weekday_4</th>\n",
       "      <th>weekday_5</th>\n",
       "      <th>weekday_6</th>\n",
       "      <th>holiday</th>\n",
       "      <th>workingday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   season_1  season_2  season_3  season_4  mnth_1  mnth_2  mnth_3  mnth_4  \\\n",
       "0         1         0         0         0       1       0       0       0   \n",
       "1         1         0         0         0       1       0       0       0   \n",
       "2         1         0         0         0       1       0       0       0   \n",
       "3         1         0         0         0       1       0       0       0   \n",
       "4         1         0         0         0       1       0       0       0   \n",
       "\n",
       "   mnth_5  mnth_6     ...      weathersit_3  weekday_0  weekday_1  weekday_2  \\\n",
       "0       0       0     ...                 0          0          0          0   \n",
       "1       0       0     ...                 0          1          0          0   \n",
       "2       0       0     ...                 0          0          1          0   \n",
       "3       0       0     ...                 0          0          0          1   \n",
       "4       0       0     ...                 0          0          0          0   \n",
       "\n",
       "   weekday_3  weekday_4  weekday_5  weekday_6  holiday  workingday  \n",
       "0          0          0          0          1        0           0  \n",
       "1          0          0          0          0        0           0  \n",
       "2          0          0          0          0        0           1  \n",
       "3          0          0          0          0        0           1  \n",
       "4          1          0          0          0        0           1  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_data_cat = pd.concat([categorical_data_cat, data['holiday'],  data['workingday']], axis = 1, ignore_index=False)\n",
    "categorical_data_cat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.355170</td>\n",
       "      <td>0.373517</td>\n",
       "      <td>0.828620</td>\n",
       "      <td>0.284606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.379232</td>\n",
       "      <td>0.360541</td>\n",
       "      <td>0.715771</td>\n",
       "      <td>0.466215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.171000</td>\n",
       "      <td>0.144830</td>\n",
       "      <td>0.449638</td>\n",
       "      <td>0.465740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.175530</td>\n",
       "      <td>0.174649</td>\n",
       "      <td>0.607131</td>\n",
       "      <td>0.284297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.209120</td>\n",
       "      <td>0.197158</td>\n",
       "      <td>0.449313</td>\n",
       "      <td>0.339143</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       temp     atemp       hum  windspeed\n",
       "0  0.355170  0.373517  0.828620   0.284606\n",
       "1  0.379232  0.360541  0.715771   0.466215\n",
       "2  0.171000  0.144830  0.449638   0.465740\n",
       "3  0.175530  0.174649  0.607131   0.284297\n",
       "4  0.209120  0.197158  0.449313   0.339143"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#数字化特征 归一化\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "mn_x = MinMaxScaler()\n",
    "numerical_features = ['temp','atemp','hum','windspeed']\n",
    "temp = mn_x.fit_transform(data[numerical_features])\n",
    "\n",
    "numerical_data_cat = pd.DataFrame(data=temp, columns=numerical_features, index =data.index)\n",
    "numerical_data_cat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>season_1</th>\n",
       "      <th>season_2</th>\n",
       "      <th>season_3</th>\n",
       "      <th>season_4</th>\n",
       "      <th>mnth_1</th>\n",
       "      <th>mnth_2</th>\n",
       "      <th>mnth_3</th>\n",
       "      <th>mnth_4</th>\n",
       "      <th>mnth_5</th>\n",
       "      <th>...</th>\n",
       "      <th>weekday_5</th>\n",
       "      <th>weekday_6</th>\n",
       "      <th>holiday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>yr</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.355170</td>\n",
       "      <td>0.373517</td>\n",
       "      <td>0.828620</td>\n",
       "      <td>0.284606</td>\n",
       "      <td>0</td>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.379232</td>\n",
       "      <td>0.360541</td>\n",
       "      <td>0.715771</td>\n",
       "      <td>0.466215</td>\n",
       "      <td>0</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.171000</td>\n",
       "      <td>0.144830</td>\n",
       "      <td>0.449638</td>\n",
       "      <td>0.465740</td>\n",
       "      <td>0</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.175530</td>\n",
       "      <td>0.174649</td>\n",
       "      <td>0.607131</td>\n",
       "      <td>0.284297</td>\n",
       "      <td>0</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.209120</td>\n",
       "      <td>0.197158</td>\n",
       "      <td>0.449313</td>\n",
       "      <td>0.339143</td>\n",
       "      <td>0</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant  season_1  season_2  season_3  season_4  mnth_1  mnth_2  mnth_3  \\\n",
       "0        1         1         0         0         0       1       0       0   \n",
       "1        2         1         0         0         0       1       0       0   \n",
       "2        3         1         0         0         0       1       0       0   \n",
       "3        4         1         0         0         0       1       0       0   \n",
       "4        5         1         0         0         0       1       0       0   \n",
       "\n",
       "   mnth_4  mnth_5  ...   weekday_5  weekday_6  holiday  workingday      temp  \\\n",
       "0       0       0  ...           0          1        0           0  0.355170   \n",
       "1       0       0  ...           0          0        0           0  0.379232   \n",
       "2       0       0  ...           0          0        0           1  0.171000   \n",
       "3       0       0  ...           0          0        0           1  0.175530   \n",
       "4       0       0  ...           0          0        0           1  0.209120   \n",
       "\n",
       "      atemp       hum  windspeed  yr   cnt  \n",
       "0  0.373517  0.828620   0.284606   0   985  \n",
       "1  0.360541  0.715771   0.466215   0   801  \n",
       "2  0.144830  0.449638   0.465740   0  1349  \n",
       "3  0.174649  0.607131   0.284297   0  1562  \n",
       "4  0.197158  0.449313   0.339143   0  1600  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fe_data = pd.concat([data['instant'],categorical_data_cat,numerical_data_cat,data['yr'],data['cnt']],axis = 1)\n",
    "fe_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 731 entries, 0 to 730\n",
      "Data columns (total 35 columns):\n",
      "instant         731 non-null int64\n",
      "season_1        731 non-null uint8\n",
      "season_2        731 non-null uint8\n",
      "season_3        731 non-null uint8\n",
      "season_4        731 non-null uint8\n",
      "mnth_1          731 non-null uint8\n",
      "mnth_2          731 non-null uint8\n",
      "mnth_3          731 non-null uint8\n",
      "mnth_4          731 non-null uint8\n",
      "mnth_5          731 non-null uint8\n",
      "mnth_6          731 non-null uint8\n",
      "mnth_7          731 non-null uint8\n",
      "mnth_8          731 non-null uint8\n",
      "mnth_9          731 non-null uint8\n",
      "mnth_10         731 non-null uint8\n",
      "mnth_11         731 non-null uint8\n",
      "mnth_12         731 non-null uint8\n",
      "weathersit_1    731 non-null uint8\n",
      "weathersit_2    731 non-null uint8\n",
      "weathersit_3    731 non-null uint8\n",
      "weekday_0       731 non-null uint8\n",
      "weekday_1       731 non-null uint8\n",
      "weekday_2       731 non-null uint8\n",
      "weekday_3       731 non-null uint8\n",
      "weekday_4       731 non-null uint8\n",
      "weekday_5       731 non-null uint8\n",
      "weekday_6       731 non-null uint8\n",
      "holiday         731 non-null int64\n",
      "workingday      731 non-null int64\n",
      "temp            731 non-null float64\n",
      "atemp           731 non-null float64\n",
      "hum             731 non-null float64\n",
      "windspeed       731 non-null float64\n",
      "yr              731 non-null int64\n",
      "cnt             731 non-null int64\n",
      "dtypes: float64(4), int64(5), uint8(26)\n",
      "memory usage: 70.0 KB\n"
     ]
    }
   ],
   "source": [
    "fe_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fe_data.to_csv('fe_day.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
